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Section 1
Planning the data pipelines
Preview
Preview: Ingesting and processing the data (~25%)
Preview includes- 4 of 5 lesson topics
- 1 overview segment
- 3 core concepts
- 2 exam tips
Lesson Topics
- Pipeline Definition and Scope
- Unbounded Data Windowing
- Watermarks and Triggering
- Pub/Sub Messaging Concepts
Overview
This objective focuses on planning Apache Beam pipelines, specifically considering how to process unbounded streaming data and manage delivery/recovery scenarios.
Core Concepts
- Beam pipelines orchestrate transforms on distributed data.
- Unbounded data processing necessitates windowing, watermarks, triggers, and allowed lateness to manage grouping and emission.
- Pub/Sub enables decoupling of publishers and subscribers via topics and subscriptions.
Exam Tips
- Carefully analyze the data stream characteristics (bounded vs. unbounded) to determine the appropriate windowing strategy.
- Understand the impact of different delivery and recovery options on pipeline resilience and data consistency.
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Section 2
Designing for security and compliance
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Section 3
Selecting storage systems
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Section 4
Optimizing resources
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Section 5
Preparing data for visualization
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Section 6
Building the pipelines
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Section 7
Designing for reliability and fidelity
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Section 8
Planning for using a data warehouse
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Section 9
Designing automation and repeatability
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Section 10
Deploying and operationalizing the pipelines
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Section 11
Preparing data for AI and ML
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Section 12
Designing for flexibility and portability
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Section 13
Using a data lake
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Section 14
Organizing workloads based on business requirements
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Section 15
Designing data migrations
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Section 16
Sharing data
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Section 17
Designing for a data platform
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Section 18
Monitoring and troubleshooting processes
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Section 19
Maintaining awareness of failures and mitigating impact
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